European Journals of Emerging Computer Vision and Natural Language Processing
A-Z Journals

Aim & Scope

Aim

The European Journal of Emerging Computer Vision and Natural Language Processing (EJECVNLP) aims to advance state-of-the-art research, innovation, and development in the fields of computer vision and natural language processing. The journal provides a global platform for scholars, engineers, scientists, and industry experts to publish high-quality research focusing on modern machine perception, language intelligence, and human-AI interaction.

EJECVNLP is committed to fostering knowledge exchange, accelerating scientific progress, and supporting ethical, explainable, and socially responsible AI technologies that solve real-world problems.

Scope

EJECVNLP welcomes original research, reviews, case studies, and applied research contributions in all areas related to computer vision, natural language processing, and artificial intelligence.

Core Focus Areas

Computer Vision

  • Image & video processing

  • Object detection, recognition & tracking

  • Scene understanding & 3D vision

  • Facial analysis & human activity recognition

  • Medical imaging & diagnostic vision systems

  • Optical flow, motion analysis & depth estimation

  • Visual transformers & deep vision architectures

  • Edge-AI, embedded vision & real-time vision systems

Natural Language Processing

  • Language modeling & large language models (LLMs)

  • Speech recognition and spoken language technology

  • Text mining, information extraction & retrieval

  • Machine translation & multilingual NLP

  • Sentiment analysis & opinion mining

  • Conversational AI, chatbots & dialogue systems

  • NLP for healthcare, law, finance & social media

  • Low-resource language processing & language preservation

Multimodal & Emerging AI

  • Vision-Language models (VLMs)

  • Multimodal learning & cross-modal representation

  • AI-powered robotics & perception systems

  • Reinforcement learning in vision/NLP tasks

  • Generative AI for vision & language (GANs, Diffusion Models, LLM+Vision)

  • Explainable and responsible AI

  • Robustness, security & adversarial learning

Article Types Accepted

  • Original research papers

  • Review & survey articles

  • Short communications & rapid results

  • Dataset & benchmark papers

  • Applied/industry case studies

  • Algorithmic innovations & model papers

  • Perspective & technology trend articles

Ethical & Editorial Policy

EJECVNLP follows rigorous academic standards through:

  • Double-blind peer review

  • COPE-based publication ethics

  • International editorial and reviewer board

  • DOI assignment & indexing initiatives

  • Transparent review & timely publication process

Ethical AI, data transparency, and responsible research are core priorities.

Target Audience

  • AI researchers & data scientists

  • Computer vision & NLP engineers

  • University professors & doctoral researchers

  • Research labs, AI startups & tech industry professionals

  • Policy experts & ethical AI researchers